Multi-Robot Exploration and Semantic Map Building: Heterogeneous Terrestrial Robots and a Drone

Abstract

In this work, we propose motion strategies for exploration and mapping of an unknown environment with a team of heterogeneous robots composed of two ground robots with different sensing and motion capabilities and a drone. Our proposal is based on stochastic dynamic programming with incomplete information; we improve this technique, generating a new approach that requires significantly fewer computations. We also propose new observation and motion models that generate a map that combines geometric and semantic information. For dealing with real data obtained with a video camera and a laser, we use machine learning techniques for building such a geometric-semantic map. Experiments are presented and analyzed in simulation and on real robots. We compare the approach with other strategies reported in the literature, showing that our approach requires shorter paths and fewer sensing locations to explore the environment, thus demonstrating the effectiveness of this approach.